Systems and methods for predicting and detecting a cardiac event

ABSTRACT

Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/040,473, filed Jul. 19, 2018, entitled “SYSTEMS AND METHODS FORPREDICTING AND DETECTING A CARDIAC EVENT,” the entire contents of whichis incorporated herein by reference.

TECHNICAL FIELD

The disclosed technology relates generally to systems and methods forpredicting and detecting cardiac events, and more particularly, someembodiments relate to systems and methods for predicting and detectingcardiac events in non-clinical noisy environments such as, in-vehicleenvironments, based on real-time biomedical signals received for driversand/or passengers within the vehicle.

DESCRIPTION OF THE RELATED ART

Conventional systems for detecting or predicting cardiac events aretypically limited to specific types of cardiac events (or other medicalconditions) and are difficult to modify to identify other types ofevents. Conventional systems are also often over-reliant on identifyingparticular peaks in a biomedical signal (e.g., an ECG signal) and failto consider motion of the subject or other factors such as theenvironment noise, in detecting or predicting a potential medicalcondition. Additionally, conventional systems are typically designed toeither detect or predict a medical condition (such as a cardiac event),but are rarely able to do both. There is a need for a means to bothreliably detect and predict multiple types of medical conditionssimultaneously.

BRIEF SUMMARY OF EMBODIMENTS

The systems and methods described herein may be used to detect and/orpredict cardiac events based on real-time biomedical signals obtainedfrom an individual. In various embodiments, a machine learning algorithmmay be used to detect and/or predict the cardiac events. Training dataobtained may comprise of a plurality of biomedical signals (“trainingsignals”). Pre-event signals may be extracted from the training datathat span a predetermined time interval before occurrence of aparticular cardiac event. The training data may be analyzed to compute asequence of conditional probability vectors. The pre-event signals maybe applied to a Markov chain algorithm to train the machine learningalgorithm based on the sequence of probability vectors obtained byanalyzing the training signals, resulting in trained Markov transitionmatrices. The trained Markov transition matrices may be applied to asignal obtained from an individual in real-time to automatically predictan upcoming cardiac event for that individual. In various embodiments,the training signals and signals obtained in real-time may bepre-processed to remove noisy signals and/or enhance the signalultimately utilized.

Unlike conventional systems which rely on recognized peaks associatedwith a signal (e.g., R-peak in an ECG signal), the systems and methodsdescribed herein do not rely on peak detection to analyze a signal. Thesystems and methods described herein instead identify certain forms in asignal and the frequency with which the form appears to identifypatterns. The identified patterns are assigned letters (i.e., encodedinto distributions of letters, such as distribution “Q”). In variousembodiments, identified patterns may be encoded as letters, numbers,and/or other identifiers. Based on the known morphology of a signal, theMarkov model is able to identify states in the signal based on thedistribution of letters in the signal. For example, the Markov model mayconsider domain knowledge associated with one or more medical conditionsand biomedical signals to identify states associated with a particularpattern. For example, based on the temporal relationship betweenmultiple patterns in a ECG signal, the Markov model may be configured todetermine that a first pattern assigned the arbitrary letter “x” is anR-peak and that a second pattern assigned the arbitrary letter “y” is apeaked P-wave. As such, the systems and methods described herein do notrely on peak detection, but instead are able to analyze a signal basedon the patterns identified therein.

In some embodiments, the systems and methods described herein may beconfigured to generate an alert and cause the alert to be communicatedwhen a cardiac event has been predicted or detected. In someembodiments, the systems and methods described herein may be configuredto predict and/or detect cardiac events in an in-vehicle environmentbased on real-time biomedical signals received for drivers and/orpassengers within the vehicle. In the in-vehicle environment, thesystems and methods described herein may be configured to generate analert and cause the alert to be provided to a driver, a passenger,and/or the authorities. In some embodiments, the system mayautomatically slow the vehicle and/or actuate other safety maneuvers inresponse to a predicted or detected cardiac event.

The system may include one or more hardware processors configured bymachine-readable instructions. Executing the machine-readableinstructions may cause the one or more processors to predict and/ordetect cardiac events based on real-time biomedical signals obtainedfrom an individual. The one or more physical processors may representprocessing functionality of multiple components of the system operatingin coordination. Therefore, the various processing functionalitydescribed in relation to the one or more processors may be performed bya single component or by multiple components of the system.

Other features and aspects of the disclosed technology will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, which illustrate, by way of example, thefeatures in accordance with embodiments of the disclosed technology. Thesummary is not intended to limit the scope of any inventions describedherein, which are defined solely by the claims attached hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

The technology disclosed herein, in accordance with one or more variousembodiments, is described in detail with reference to the followingfigures. The drawings are provided for purposes of illustration only andmerely depict typical or example embodiments of the disclosedtechnology. These drawings are provided to facilitate the reader'sunderstanding of the disclosed technology and shall not be consideredlimiting of the breadth, scope, or applicability thereof. It should benoted that for clarity and ease of illustration these drawings are notnecessarily made to scale.

FIG. 1 illustrates an example of a vehicle with which systems andmethods for predicting and/or detecting cardiac events may beimplemented, in accordance with one embodiment of the systems andmethods described herein.

FIG. 2 contains an operational flow diagram illustrating an exampleworkflow for training a model used to predict and/or detect a cardiacevent, in accordance with various embodiments.

FIG. 3 contains an operational flow diagram illustrating an exampleworkflow for classifying conditions based on one or more biomedicalsignals, in accordance with various embodiments.

FIG. 4A contains an operational flow diagram illustrating an exampleworkflow for pre-processing a biomedical signal, in accordance withvarious embodiments.

FIG. 4B illustrates an example of a biomedical signal at differentstages of filtering and encoding, in accordance with variousembodiments.

FIG. 5 contains an operational flow diagram illustrating an exampleworkflow for creating a Markov chain algorithm, in accordance withvarious embodiments.

FIG. 6A illustrates an example of a relational map between words that issearched to create a Markov chain algorithm, in accordance with variousembodiments.

FIG. 6B illustrates an example of a Markov chain algorithm created basedon the relational map between words depicted in FIG. 6A, in accordancewith various embodiments.

FIG. 7 is an example of a method for predicting and/or detecting cardiacevents, in accordance with various embodiments.

FIG. 8 illustrates an example computing module that may be used inimplementing various features of embodiments of the disclosedtechnology.

FIG. 9 illustrates an example of a block diagram of an event predictionmodule configured to predict and/or detect medical conditions based onone or more obtained biomedical signals, in accordance with variousembodiments.

The figures are not intended to be exhaustive or to limit the inventionto the precise form disclosed. It should be understood that theinvention can be practiced with modification and alteration, and thatthe disclosed technology be limited only by the claims and theequivalents thereof.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Embodiments of the technology disclosed herein are directed towardsystems and methods for predicting and/or detecting medical conditionsof an individual based on one or more obtained biomedical signals. Invarious embodiments, the systems and methods disclosed herein may beconfigured to predict and/or detect medical conditions (e.g., a cardiacevent) in an in-vehicle environment based on real-time biomedicalsignals received for drivers and/or passengers within the vehicle.

In various embodiments, the systems and methods disclosed herein may beconfigured to predict and/or detect cardiac events, such as AtrialFibrillation (or AFib) events, in real-time based on biomedical signalsreceived for an individual. Atrial Fibrillation is a common cardiacarrhythmia with symptoms ranging from nonexistent to severe. Predictionof abnormal heart rates or AFib events may help prevent life-threateningconditions like stroke or heart failure. Typically, monitoring a patientwith AFib requires monitoring an electrocardiogram (ECG) signal of thepatient. An ECG signal comprises numerous components, including a P-wave(representing depolarization of the atria), the QRS complex and R-peak(representing the rapid depolarization of the right and leftventricles), and the T-wave (representing the repolarization of theventricles).

Most existing monitoring systems require long-term hospitalization. Thesystems and methods described herein are directed to systems and methodsthat may be used to predict and/or detect cardiac events, such asepisodes associated with AFib, ventricular fibrillation, ventriculartachycardia, supraventricular tachycardia, bradycardia, myocardialinfarction, and/or one or more other types of cardiac events. Thesystems and methods described herein may be used to predict and/ordetect cardiac events either inside or outside of a hospitalenvironment. For example, the systems and methods described herein maybe implemented in an environment remote from a hospital where cliniciansable to diagnose a cardiac event are not available, in the home, in thehospital to assist clinicians, in an in-vehicle environment where suddencardiac events can be dangerous to the drivers, the passengers, andother individuals on the road, and/or one or more other environmentsinside or outside a hospital environment.

In some embodiments, the technology disclosed herein may be implementedin any of a number of different vehicle types including, for example,automobiles, trucks, buses, boats and ships, and other vehicles. Anexample vehicle, such as a hybrid electric vehicle (HEV), in which acamera vehicle activation system may be implemented is illustrated inFIG. 1. FIG. 1 illustrates an example of a hybrid electric vehicle 10that may include an internal combustion engine 14 and one or moreelectric motors 12 as sources of motive power. Driving force generatedby the internal combustion engine 14 and motor 12 can be transmitted toone or more wheels 34 via a torque converter 16, a transmission 18, adifferential gear device 28, and a pair of axles 30.

As an HEV, vehicle 10 may be driven/powered with either or both ofengine 14 and the motor(s) 12 as the drive source for travel. Forexample, a first travel mode may be an engine-only travel mode that onlyuses ICE 14 as the drive source for travel. A second travel mode may bean EV travel mode that only uses the motor(s) 12 as the drive source fortravel. A third travel mode may be an HEV travel mode that uses engine14 and the motor(s) 12 as drive sources for travel. In the engine-onlyand HEV travel modes, vehicle 10 relies on the motive force generated atleast by ICE 14, and a clutch 15 may be included to engage engine 14. Inthe EV travel mode, vehicle 10 is powered by the motive force generatedby motor 12 while engine 14 may be stopped and clutch 15 disengaged.

Engine 14 can be an internal combustion engine such as a gasoline,diesel or similarly powered engine in which fuel is injected into andcombusted in a combustion chamber. An output control circuit 14A may beprovided to control drive (output torque) of engine 14. Output controlcircuit 14A may include a throttle actuator to control an electronicthrottle valve that controls fuel injection, an ignition device thatcontrols ignition timing, and the like. Output control circuit 14A mayexecute output control of engine 14 according to a command controlsignal(s) supplied from an electronic control unit 50, described below.Such output control can include, for example, throttle control, fuelinjection control, and ignition timing control.

Motor 12 can also be used to provide motive power in vehicle 10, and ispowered electrically via a power storage device 44. Motor 12 can bepowered by power storage device 44 to generate a motive force to movethe vehicle and adjust vehicle speed. Motor 12 can also function as agenerator to generate electrical power such as, for example, whencoasting or braking. Power storage device 44 may also be used to powerother electrical or electronic systems in the vehicle. Motor 12 may beconnected to power storage device 44 via an inverter 42. Power storagedevice 44 can include, for example, one or more batteries, capacitivestorage units, or other storage reservoirs suitable for storingelectrical energy that can be used to power one or more motors 12. Whenpower storage device 44 is implemented using one or more batteries, thebatteries can include, for example, nickel metal hydride batteries,lithium ion batteries, lead acid batteries, nickel cadmium batteries,lithium ion polymer batteries, and other types of batteries.

An electronic control unit 50 (described below) may be included and maycontrol the electric drive components of the vehicle as well as othervehicle components. For example, electronic control unit 50 may controlinverter 42, adjust driving current supplied to motors and adjust thecurrent received from motors 12 during regenerative coasting andbreaking. As a more particular example, output torque of the motor 12can be increased or decreased by electronic control unit 50 through theinverter 42.

A torque converter 16 can be included to control the application ofpower from engine 14 and motors 12 to transmission 18. Clutch 15 can beincluded to engage and disengage engine 14 from the drivetrain of thevehicle. In the illustrated example, a crankshaft 32, which is an outputmember of engine 14, may be selectively coupled to the motors 12 andtorque converter 16 via clutch 15.

As alluded to above, vehicle 10 may include an electronic control unit50. Electronic control unit 50 may include circuitry to control variousaspects of the vehicle operation. Electronic control unit 50 mayinclude, for example, a microcomputer that includes one or moreprocessing units (e.g., microprocessors), memory storage (e.g., RAM,ROM, etc.), and I/O devices. The processing units of electronic controlunit 50, execute instructions stored in memory to control one or moreelectrical systems or subsystems in the vehicle. Electronic control unit50 can include a plurality of electronic control units such as, forexample, an electronic engine control module, a powertrain controlmodule, a transmission control module, a suspension control module, abody control module, and so on. As a further example, electronic controlunits can be included to control systems and functions such as doors anddoor locking, lighting, human-machine interfaces, cruise control,telematics, braking systems (e.g., ABS or ESC), battery managementsystems, and so on. These various control units can be implemented usingtwo or more separate electronic control units, or using a singleelectronic control unit.

In various embodiments, electronic control unit 50 may include or becommunicatively coupled to an event prediction module, such as eventprediction module 900 depicted in FIG. 9. In some embodiments, the eventprediction module may interface with electronic control unit 50 and/orone or more other in-vehicle components. In some embodiments, electroniccontrol unit 50 may be configured control one or more in-vehiclecomponents based on a medical condition predicted or detected by eventprediction module. For example, electronic control unit 50 may beconfigured to cause an alert to be communicated in response to theprediction or detection of a cardiac event by event prediction module.In some embodiments, electronic control unit 50 may be configured togenerate an alert and cause the alert to be provided to a driver, apassenger, and/or the authorities in response to the prediction ordetection of a cardiac event by event prediction module. For example,electronic control unit 50 may be configured to automatically cause analert to be displayed via an in-vehicle display of the vehicle. In someembodiments, an alert may be provided to other individuals on or nearthe roadway via the activation of emergency lights on the vehicle. Insome embodiments, electronic control unit 50 may be configured toautomatically slow the vehicle in response to the prediction ordetection of a cardiac event by event prediction module.

In the example illustrated in FIG. 1, electronic control unit 50receives information from a plurality of sensors included in vehicle 10.For example, electronic control unit 50 may receive signals thatindicate vehicle operating conditions or characteristics, signals thatcan be used to derive vehicle operating conditions or characteristics,signals that indicate a medical condition of a driver and/orpassenger(s) of vehicle 10 (i.e., one or more biomedical signals),and/or one or more other signals. These may include, but are not limitedto accelerator operation amount, A_(CC), a revolution speed, N_(E), ofICE 14 (engine RPM), a rotational speed, N_(MS), of the motor 12 (motorrotational speed), and vehicle speed, N_(V). These may also includetorque converter 16 output, N_(T) (e.g., output amps indicative of motoroutput), brake operation amount, B, battery SOC (i.e., the chargedamount for battery 44 detected by an SOC sensor). Accordingly, vehicle10 can include a plurality of sensors 52 that can be used to detectvarious conditions internal or external to the vehicle and providesensed conditions to engine control unit 50 (which, again, may beimplemented as one or a plurality of individual control circuits). Inone embodiment, sensors 52 may be included to detect one or moreconditions directly or indirectly such as, for example, fuel efficiency,E_(F), motor efficiency, E_(MG), hybrid (ICE 14+motor 12) efficiency,etc.

In some embodiments, one or more of the sensors 52 may include their ownprocessing capability to compute the results for additional informationthat can be provided to electronic control unit 50. In otherembodiments, one or more sensors may be data-gathering-only sensors thatprovide only raw data to electronic control unit 50. In furtherembodiments, hybrid sensors may be included that provide a combinationof raw data and processed data to electronic control unit 50. Sensor 52may provide an analog output or a digital output.

Additional sensors 52 may be included to detect vehicle conditions aswell as to detect external conditions. Sensors that might be used todetect external conditions can include, for example, sonar, radar, lidaror other vehicle proximity sensors, and cameras or other image sensors.Cameras, or image capture devices can be used to detect, for example,road conditions in the region in front and all around the vehicle and soon. Still other sensors may include those that can detect road grade.While some sensors can be used to actively detect passive environmentalobjects, other sensors can be included and used to detect active objectssuch as those objects used to implement smart roadways that may activelytransmit and/or receive data or other information.

In various implementations, one or more sensors configured to monitor abiomedical signal of a driver and/or passenger(s) of vehicle 10 may beincorporated into vehicle 10. For example, a sensor configured tomonitor a biomedical signal (e.g., an ECG signal) may be incorporatedinto a steering wheel, a seat, and/or other components in contact withor within a proximity of the driver and/or passenger(s). Biomedicalsignals obtained via one or more sensors incorporated into vehicle 10may be provided to the event prediction module.

Event prediction module may be configured to predict and/or detect oneor more medical conditions based on obtained biomedical signals. Invarious embodiments, event prediction module may be configured toutilize a machine learning algorithm to predict and/or detect one ormore medical conditions based on obtained biomedical signals. The eventprediction module may be configured to obtain training data comprising aplurality of biomedical signals (“training signals”). The eventprediction module may be configured to extract pre-event signals fromthe training data that span a predetermined time interval before aparticular cardiac event. The event prediction module may be configuredto analyze the training data to compute conditional probability vectors.The event prediction module may be configured to apply the pre-eventsignals to a Markov chain algorithm to train the machine learningalgorithm based on the sequence of probability vectors obtained byanalyzing the training signals, resulting in trained Markov transitionmatrices. The event prediction module may be configured to apply thetrained Markov transition matrices to a signal obtained from anindividual in real-time to automatically predict an upcoming cardiacevent for the individual. In some embodiments, the training signals andsignals obtained in real-time may be pre-processed to remove noisysignals and/or enhance the signal ultimately utilized.

In some embodiments, the event prediction module may be configured topredict and/or detect one or more medical conditions based on a singlebiomedical signal or multiple biomedical signals simultaneously. Forexample, the event prediction module may be configured to predict and/ordetect one or more medical conditions based on ECG signals and/orphotoplethysmogram (PPG) signals simultaneously. In various embodiments,the event prediction module described herein may be the same or similarto event prediction module 900 depicted in FIG. 9.

In various exemplary embodiments, the biomedical signals may compriseelectrocardiogram (ECG) signals, and the ECG signals may be utilized byevent prediction module to predict and/or detect one or more cardiacevents, such as an episode of Atrial Fibrillation (AFib). This is anon-limiting example, and the features of the systems and methodsdescribed herein may utilize one or more other types of biomedicalsignals to detect other medical conditions.

In an exemplary embodiment, event prediction module may be configured totrain a Markov model and associated classifications by encoding abiomedical signal (e.g., an ECG signal) as word distributions. Forexample, a finite alphabet “Σ” may be used, in which “Σ*” is the set ofall words in the alphabet “Σ”, including the empty word “E”. The lengthof a word “(Ω∈Σ*” is denoted by “l(ω)”. If “Σ={α₁, α₂ . . . α_(d)}” isan alphabet, then a word distribution of length “n” is a “d×n” matrix“Q”. For example, a random word “ω=ω₁ω₂ . . .

” of length “n” has probability distribution “Q” if the letters “ω₁, ω₂,. . . , ω_(n)” are independent of each other, and“Q_(i,j)=P(ω_(j)=a_(i)” is the probability that the “j-th” letter of theword “ω” is the letter “a₁”. For a given word “

” of length “

”, the probability “P(ω=u)” that “ω” is equal to “

=a_(i) ₁ a_(i) ₂ . . . a_(i) _(n) ” is equal to “Π_(j=1) ^(n) Q_(i) _(j)_(,j)”.

In an exemplary embodiment, the Markov model utilized by eventprediction module may comprise Markov Model “M”. Markov Model “M” is aset “S” of states, an alphabet “Σ”, together with a transition function“T:SΣ→S” and a probability function “P: S×Σ→[0,1]”. If in state “s∈S”,then with probability “P(s, a)”, the model will transition to the state“T(s, a)” and produce the letter “a” as output. An initial state “ι∈S”may be specified. For a word “w”, “M(w)” may be the state of theautomaton if beginning in the initial state and the output of the outputis the word “w”. Accordingly, “M(ϵ)=└” and

M(w ₁ w ₂ . . . w _(k))=T(M(w ₁ w ₂ . . . w _(k−1)),w _(k)).

The Markov Model can be thought of as a random word generator. Theprobability that Markov model “M” produces the output “w” after “n=l(w)”steps starting at the initial state will be denoted by “P(w|M)”. Thealphabet may be represented by “Σ={a₁, a₂, . . . , a_(d)}”. The set ofstates may be represented by “S={s₁=ϵ, s₂, . . . , s_(m)}”. Thefunctions “P” and “T” may be represented by “m×d” matrices “P” and “T”so that “P_(ij)=P(s_(i), a_(j))” for all “i” and “j”, and “T_(ij)=k” if“T(s_(i), a_(j))=s_(k)” for all “i,j,k”.

In various embodiments, the event prediction module may be configured toobtain training data comprising a plurality of biomedical signals(“training signals”). In some embodiments, the event prediction modulemay be configured to obtain training data from a database associatedwith a particular biomedical signal. In an exemplary embodiment in whichthe biomedical signal comprises an ECG signal, the training data may beobtained from the Physionet/CinC 2017 database, and/or one or more otherdatabases that store ECG signals. In some embodiments, signals obtainedfrom individuals in real-time may be stored and later used to train amachine learning algorithm to predict and/or detect one or more medicalconditions. For example, signals obtained from individuals in real-timemay be stored as training data in main memory (e.g., main memory 808)and/or one or more storage devices (e.g., storage devices 810).

In various embodiments, the event prediction module may be configured totrain a model used to predict and/or detect a cardiac event. Referringto FIG. 2, an operational flow diagram is depicted illustrating anexample workflow 200 for training the model used to predict and/ordetect one or more medical conditions, in accordance with variousembodiments. In various implementations, the event prediction module maybe configured to predict and/or detect several types of cardiac events,which may be denoted CE₁, CE₂, . . . , CE_(r). For example, CE₁ maycomprise atrial fibrillation, CE₂ may comprise ventricular tachycardia,and CE₃, . . . , CE_(r) may comprise one or more other types of cardiacevents. The event prediction module may be configured to obtain trainingdata (e.g., training ECGs). The obtained training data may be used totrain the model. For example, the training data may comprise one or morebiomedical signals from healthy individuals and one or more biomedicalsignals from unhealthy individuals (e.g., ECGs related to CE₁, CE₂, . .. , CE_(r)). In various embodiments, biomedical signals from multiplehealthy individuals and multiple unhealthy individuals may be utilizedas training data simultaneously. In an exemplary embodiment (as depictedin FIG. 2), the training data may comprise one or more training ECGsignals. In various embodiments, the features described herein to trainthe model used to predict and/or detect one or more medical conditionsmay be performed by training component 902 of event prediction module900 depicted in FIG. 9.

Referring back to FIG. 2, the event prediction module may be configuredto extract windows that precede a future cardiac event (CE) and windowsthat do not contain or precede cardiac events. In an exemplaryembodiment, the event prediction module may be configured to extractwindows that are indicative of future AFib episodes (Pre-AFib ECGs) andwindows that are normal (Normal ECGs). In some embodiments, the eventprediction module may be configured to extract pre-event signals fromthe training data that span a predetermined time interval before aparticular cardiac event. For example, an ECG signal more than apredetermined time interval (e.g., 2 minutes) prior to a cardiac eventmay not provide any indication of the oncoming cardiac event. As such,the ECG signal more than the predetermined time interval prior to thecardiac event may not be relevant training data. In various embodiments,the event prediction module may be configured to extract only thesignals from the training data that span the predetermined time intervalbefore a particular cardiac event and signals for a window that arenormal that is of the same length as the predetermined time interval.Referring to FIG. 2, the event prediction module may be configured topredict and/or detect two cardiac events (i.e., where r=2). In theembodiment of FIG. 2, the event prediction module may be configured toextract from signals related to CE₁ a predetermined time interval priorto the cardiac event CE₁ (denoted CE₁ECGs), extract from signals relatedto CE₂ the predetermined time interval prior to the cardiac event CE₂(denoted CE₂ECGs), and extract from healthy signals the predeterminedtime interval (denoted Normal ECGs). In various implementations, eventprediction module may be configured to encode extracted signals (e.g.,CE₁ECGs, CE₂ECGs, and Normal ECGs) into word distributions. Encodedsignals related to future cardiac event (CE) (i.e., CE₁, CE₂, . . . ,CE_(r)) are utilized to create Markov models for the cardiac events. Forexample, event prediction module may be configured to utilize theencoded CE₁ECGs to create a Markov model “M_(CE1)”, and utilize theencoded CE₂ECGs to create a Markov model “M_(CE2) ”. The eventprediction module may be configured to utilize the encoded Normal ECGsto create a Markov model “M_(n)”. In some embodiments, the encoded ECGsare utilized to create Markov models as described in example workflow500 for creating a Markov chain algorithm depicted in FIG. 5.

In various embodiments, event prediction module may be configured toclassify normal medical conditions (i.e., corresponding to a healthyindividual) based on a Markov model created utilizing normal ECGs (e.g.,“M_(n)”) and classify non-normal medical conditions (i.e., correspondingto an unhealthy individual) based on the Markov models created utilizingECG windows that precede a future cardiac event (CE) (e.g., “M_(CE1)”and “M_(CE2)”). For example, a non-normal medical condition may comprisea Pre-AFib state that is classified based on one or more obtainedbiomedical signals comprising at least an ECG signal. Referring to FIG.3, an operational flow diagram is depicted illustrating an exampleworkflow 300 for classifying conditions based on one or more biomedicalsignals, in accordance with various embodiments. The biomedical signalmay be obtained as described herein and stored as test data in mainmemory (e.g., main memory 808) and/or one or more storage devices (e.g.,storage devices 810). For example, the biomedical signal may comprise anECG signal. The signal may be encoded into a word distribution “Q”, asdescribed further herein. In various embodiments, the event predictionmodule may be configured to analyze the training data to computeconditional probability vectors. For example, based on the Markov models“M_(CE) ₁ ” and “M_(CE) ₂ ” for the pre-cardiac event case and theMarkov model “M_(n)” for the Normal case obtained during the trainingphase described above, event prediction module may be configured tocompute conditional probabilities that the ECG signal represents apre-cardiac event state (which may be denoted “CE₁, CE₂, . . . ,CE_(r)”) or a “Normal” state. For example, event prediction module maybe configured to compute:

P(Q|M _(CE) _(i) )=E(P(u|M _(CE) _(i) ))=ΣP(w=u|M _(CE) _(i) )P(u),

wherein “P(Q|M_(CE) _(i) )_(”) is the expected probability that theMarkov model “M_(CE) _(i) ” produced the random word “w” (withprobability distribution “Q”). Similarly, event prediction module may beconfigured to compute the expected probability “P(Q|M_(n))” that theMarkov model “M_(n)” produced the random word “w” with distribution “Q”.If “P(Q|M_(n))” is greater than “P(Q|M_(CE) _(i) )”, the eventprediction module is configured to classify the ECG as “Normal”. If“P(Q|M_(CE) _(i) )_(”) is the largest among

P(Q|M _(n)),P(Q|M _(CE) ₁ ),P(Q|M _(CE) ₂ ), . . . P(Q|M _(CE) _(r) )

the event prediction module is configured to classify the ECG as havingCE_(i). Thus, in the exemplary embodiment depicted in FIG. 3, whereinCE₁ comprises AFib and CE₂ comprises ventricular tachycardia, eventprediction module may be configured to classify a biomedical signalinput in workflow 300 as CE₁ if “P(Q|M_(CE) ₁ )” is the largest amongP(Q|M_(n)), P(Q|M_(CE) ₁ ), P(Q|M_(CE) ₂ ), and classify the biomedicalsignal input in workflow 300 as CE₂ if “P(Q|M_(CE) ₂ )” is the largestamong P(Q|M_(n)), P(Q|M_(CE1)), P(Q|M_(CE2)). In various embodiments,the features described herein to classify conditions based on one ormore biomedical signals to further train the machine learning algorithmmay be performed by classification component 904 of event predictionmodule 900 depicted in FIG. 9.

To calculate conditional probability “P(Q|M)”, the Markov model may berepresented by “M=(P, T)”, where “P” is the “m×d” probability matrix,“T” is the “m×d” transition matrix, and “Q” is a word distribution. For“k=1,2, . . . , d”, event prediction module creates a (sparse) matrix“A^((k))” of size “m×m” such that “A_(i,k) ^((j))=P_(i,j)” when“T_(i,j)=k” and all other entries are 0. Suppose that “u=u₁u₂ . . .u_(n)” is a random word with word distribution “Q”. Let

ν_(k)=(ν_(k,1)ν_(k,2) . . . ν_(k,m))ϵ

^(m)

be the probability distribution over all m states of the Markov afterreading the word u₁ . . . u_(k). This is expressed in the formula

ν_(k,i) =E(P(w ₁ . . . w _(k) =u ₁ . . . u _(k) ,M(w ₁ . . . w _(k))=s_(i))).

The initial state is ν₀=(1,0, . . . ,0), and for k>0 we have therecurrence

ν_(k)=ν_(k−1)(Q _(1,k) A ⁽¹⁾ +Q _(2,k) A ⁽²⁾ + . . . +Q _(d,k) A^((d))).

As a result,

P(Q|M)=E(P(u|M))=Σ_(j) E(P(w=u|M(w)=s _(j)))=Σ_(j)ν_(k,j)

In the foregoing implementation, P(Q|M) may be extremely small, possiblyleading to rounding errors. As a result, log P(Q|M) may instead becomputed. In some implementations, ν_(k,j)=e^(λ) ^(k) z_(k,j) whereΣ_(j)z_(k,j)=1 and λ_(k)z_(j,k) may be computed inductively, resultingin log P(Q|M)=λ_(n).

In various embodiments, event prediction module may be configured topre-process a biomedical signal that is used to predict and/or detect amedical condition (or train a model used to predict and/or detect amedical condition). For example, event prediction module may beconfigured to pre-process an ECG signal used predict and/or detect acardiac event. In various embodiments, biomedical signals may be encodedinto distributions of binary words. In some embodiments, biomedicalsignals may be encoded into distributions comprising larger alphabets.The word distributions for a signal may be based on patterns identifiedwithin the signal. Unlike conventional systems which rely on recognizedpeaks associated with a signal (e.g., R-peak in an ECG signal), thesystems and methods described herein do not rely on peak detection toanalyze a signal. The systems and methods described herein insteadidentify certain forms in a signal and the frequency with which the formappears to identify patterns. The identified patterns are assignedletters (i.e., encoded into distributions of words, such as distribution“Q”). In various embodiments, identified patterns may be encoded asletters, numbers, and/or other identifiers. Based on the knownmorphology of a signal, the Markov model is able to identify states inthe signal based on the distribution of words in the signal. Forexample, the Markov model may consider domain knowledge associated withone or more medical conditions and biomedical signals to identify statesassociated with a particular pattern. For example, based on the temporalrelationship between multiple patterns in a ECG signal, the Markov modelmay be configured to determine that a first pattern assigned thearbitrary letter “x” is an R-peak and that a second pattern assigned thearbitrary letter “y” is peaked P-wave. As such, the systems and methodsdescribed herein do not rely on peak detection, but instead are able toanalyze a signal based on the patterns identified therein.

Referring to FIG. 4A, an operational flow diagram is depictedillustrating an example workflow 400 for pre-processing a biomedicalsignal, in accordance with various embodiments. In an exemplaryembodiment, an ECG signal may be represented by “f(t)” where “t” is timein seconds, “0≤t≤t₀” and “t₀” is the length of the ECG recording. Insome embodiments, the ECG signal—“f(t)” may be considered a continuoussignal, but in some implementations it will be discrete with a largefrequency (typically ≥200 Hz). In an exemplary embodiment in which thebiomedical signal comprises an ECG signal, pre-processing may comprisebaseline removal, applying one or more morphological filters (e.g., peakfiltering, and/or one or more other morphological filters),normalization, soft thresholding, discretization, encoding the signal asa word distribution, and/or one or more other pre-processing functions.In some embodiments, the event prediction module may be configured toperform the one or more pre-processing steps to enhance the R-peaks ofthe ECG signal. FIG. 4B illustrates an example of a biomedical signal atdifferent stages of filtering and encoding, in accordance with variousembodiments. In an exemplary embodiment, the original noisy signal(“f(t)”) is represented by the original noisy signal (Signal 1) depictedin FIG. 4B.

Referring back to FIG. 4A, the event prediction module may be configuredto compute the moving average of the original signal and subtract themoving average from the original signal. For example, event predictionmodule may be configured to compute:

${{f_{av}(t)}\mspace{14mu} \text{:=}\mspace{14mu} \frac{1}{0.3}{\int_{t - {.15}}^{t + {.15}}{{f(s)}{ds}}}},$

wherein f_(av) (t) is the average value off (s) on the interval [t−0.15,t+0.15]. This value is used in the baseline removal step of signalpre-processing. Baseline removal comprises replacing “f(t)” with“f(t)−f_(av)(t)”, which removes baseline drift and waves of lowfrequency, capturing the high frequency peaks of greater significance.In an exemplary embodiment, the signal after the baseline removal stepis represented by Signal 2 depicted in FIG. 4B.

Referring back to FIG. 4A, the event prediction module is configured toapply one or more morphological filters to the signal. For example,event prediction module may comprise applying a non-linear filter to thesignal and/or one or more other morphological filters. A non-linearfilter may remove non-important noise and other variations from thesignal. For example, applying a non-linear filter to the signal maycomprise peak filtering the signal. An example of a non-linearmorphological filter that can be used to detect/predict AFib is given bythe following formula:

f _(peak)(t)=max{0,f(t)−max{f(t−0.05),f(t+0.05)}}.

The function “f_(peak)(t)” is nonnegative and it is positive if and onlyif “f(t)>f(t−0.05)” and “f(t)>f(t+0.05)”. In other words, the function“f_(peak)(t)” filters out narrow peaks of width 0.1. In an exemplaryembodiment in which the biomedical signal comprises an ECG signal, thisfilter is sensitive to R-waves, but less sensitive to T-waves andP-waves. In analyzing ECG signals for AFib, narrow R-peaks are moresignificant than T-waves. Thus, applying a non-linear filter willenhance the R-peaks, suppress the other peaks or waves (e.g., T-wavesand P-waves) and remove negative peaks. Peak filtering comprisesreplacing “f(t)” with “f_(peak)(t)”. In an exemplary embodiment, thesignal after the peak filter (“f_(peak)(t)”) is represented by Signal 3depicted in FIG. 4B. Note that the signal after the peak filter isapplied is non-negative.

Referring back to FIG. 4A, the event prediction module may be configuredto sample the biomedical signal down to a discrete signal. For example,the event prediction module may be configured to sample the biomedicalsignal down to a discrete signal “x₁, x₂, . . . , x_(n)” of 20 Hz, where“n=└t₀/20┘” and

x _(k)=max{f(t)|0.05·(k−1)≤t≤0.05·k}.

The discrete signal “x_(k)” is represents the signal afterdiscretization. In an exemplary embodiment, the signal afterdiscretization (“x_(k)”) is represented by Signal 4 depicted in FIG. 4B.

In various embodiments, the event prediction module may be configured tonormalize the signal. In an exemplary embodiment, event predictionmodule may be configured to normalize the signal by dividing by themaximum over a 2 s interval. Event prediction module may be configuredto replace “x_(k)” by “x_(k)/max{x_(k−20), x_(k−19), . . . , x_(k+20)}”.After this step, “0≤x_(k)≤1” for all “k”. The resulting signal will havepositive peaks and a maximum absolute magnitude equal to 1—thus thesignal is normalized to a scale 0 to 1. In an exemplary embodiment inwhich the biomedical signal comprises an ECG signal, if “x_(k)” is closeto 1, it likely represents an R-peak or a similar feature containedwithin the original ECG signal. If “x_(k)” is close to 0, it likely doesnot represent a peak. In an exemplary embodiment, the normalized signalis represented by Signal 5 depicted in FIG. 4B.

In some embodiments, event prediction module may be configured torenormalize a signal. For example, a sequence may be renormalizedaccording to a local relative magnitude.

In various embodiments, the event prediction module may be configured toapply a soft thresholding function to the signal. For example, eventprediction module may be configured to apply a soft thresholdingfunction to the normalized signal. In an exemplary embodiment, eventprediction module may be configured to apply a soft thresholdingfunction to the signal by replacing “x_(k)” with “ϕ(x_(k))” where “ϕ:[0,1]→[0,1]” is the function:

${\varphi (x)} = \left\{ \begin{matrix}{1\mspace{50mu}} & {{{{if}\mspace{14mu} x} \geq {.8}}\mspace{50mu}} \\{{5x} - 3} & {{{if}\mspace{14mu} {.6}} \leq x \leq {.8}} \\{0\mspace{50mu}} & {{{{if}\mspace{14mu} x} < {{.6}.}}\mspace{40mu}}\end{matrix} \right.$

In an exemplary embodiment, the signal after a soft thresholdingfunction is applied may be represented by Signal 6 depicted in FIG. 4B.

Referring back to FIG. 4A (and as previously described), a pre-processedbiomedical signal (e.g., an ECG signal) may be encoded into a worddistribution “Q”. In some embodiments, the pre-processed biomedicalsignal may be converted to an uncertain binary string before beingencoded into a word distribution “Q”. In various embodiments, thetechniques described herein to pre-process a biomedical signal(including baseline removal, peak filtering, discretization,normalization, soft thresholding, and/or other pre-processing functions)may be performed by pre-processing component 906 of event predictionmodule 900 depicted in FIG. 9.

In various embodiments, event prediction module may be configured tocreate a Markov chain algorithm. Referring to FIG. 5, an operationalflow diagram is depicted illustrating an example workflow 500 forcreating a Markov chain algorithm, in accordance with variousembodiments. In various embodiments, a parameter “C” and the worddistribution “Q” may be input. Each of the words with expected anexpected frequency greater than “C” are selected as states. Based on thewords selected as states and the computed conditional probabilityvectors, a probability matrix “P” and a transition matrix “T” arecreated. The Markov Model “M=(P,T)” is the resulting output. In variousembodiments, the features described herein to classify conditions basedon one or more biomedical signals may be performed by Markov chaincomponent 908 of event prediction module 900 depicted in FIG. 9.

For a word “u” and a random word “w”, “y(u)” may be the expected numberof times that “u” appears as a sub-word of “w”. The states of the Markovmodel are the words “u” for which “y(u)≥C”. The states are identified bydepth-first search in the tree of all words. For example, FIG. 6illustrates an example of a relational map between words (i.e., a “treeof words”) that is searched to create a Markov chain algorithm, inaccordance with various embodiments. In FIG. 6, the more lightlydepicted words (e.g., e, a, b, aa, ab, ba, aba, and bab) appear at least“C” times and are the states, and the grayed out words (e.g., ba, aaa,aab, abb, baa, abaa, abab, baba, and babb) appear <“C” times. In thisexample there will be 8 states. FIG. 7 illustrates the Markov modelobtained from the relational map between words illustrated in FIG. 6.The transition “T(a, aba)” is the largest suffix of “abaa” that is astate. Since “abaa” and “aba” are not states, we get “T(a, aba)=aa”.

The function “P” is defined by:

${P\left( {u,a,} \right)} = {\frac{\gamma \left( {ua}_{i} \right)}{\gamma (u)}.}$

This number is a conditional property, that, after “u” is read as asub-word of “w” that the next letter is “a_(i)”. The number “1−Σ_(i=1)^(d)P (u, a_(i))” is the probability that there is no next letter (i.e.,the probability that a randomly chosen appearance of “u” as a sub-word“w” the suffix of “w”. The largest suffix of ua_(i) that is still astate in “S” is defined as “T(u, a_(i))”.

In various embodiments, the event prediction module may be configured toobtain a signal from an individual in real-time. In someimplementations, the signal may be obtained from one or more sensors inan in-vehicle environment. For example, the signal may be obtained fromone or more sensors incorporated into a vehicle (e.g., vehicle 10) thatare configured to monitor a biomedical signal of a driver and/orpassenger(s) of the vehicle. In some implementations, the signal may beobtained from a portable device. For example, the signal may be obtainedfrom a wearable device affixed to the individual for which thebiomedical signal relates. In some embodiments, the signal may beobtained from medical equipment designed to obtain the signal inside oroutside of the hospital environment.

In various embodiments, the event prediction module may be configured toapply the trained Markov transition matrices to the signal obtained froman individual in real-time to automatically predict an upcoming cardiacevent for the individual. In some embodiments, the obtained signal maybe encoded as described in example workflow 300 for classifyingconditions based on one or more biomedical signals, in accordance withvarious embodiments. The signal obtained in real-time from an individualmay be encoded into a word distribution “Q”, as described furtherherein. In various embodiments, the event prediction module may beconfigured to compute conditional probability vectors for the signal.For example, event prediction module may be configured to computeconditional probabilities that the obtained ECG signal represents one ofthe pre-cardiac event states “CE₁, . . . , CE_(r)” or a normal state“n”. For example, event prediction module may be configured to compute:

P(Q|M _(CE) _(i) )=E(P(u|M _(CE) _(i) ))=ΣP(w=u|M _(CE) _(i) )P(u),

wherein “P(Q|M_(CE) _(i) )” is the expected probability that the Markovmodel “M_(CE) _(i) ” produced the random word “w” (with probabilitydistribution “Q”). Similarly, event prediction module may be configuredto compute the expected probability “P(Q|M_(n))” that the Markov model“M_(n)” produced the random word “w” with distribution “Q”. If“P(Q|M_(CE) _(i) )” is greatest among “P(Q|M_(n)), P(Q|M_(CE) ₁ ), . . ., P(Q|M_(CE) _(r) )”, the ECG is classified as “pre-cardiac eventCE_(i)”. If “P(Q|M_(n))” is greatest among “P(Q|M_(n)), P(Q|M_(CE) ₁ ),. . . , P(Q|M_(CE) _(r) )” the ECG is classified as “Normal”. In variousembodiments, the features described herein to classify conditions onsignals obtained from individuals in real-time based on one or morebiomedical signals may be performed by results component 910 of eventprediction module 900 depicted in FIG. 9.

Various features described herein are described as being performed byone or more hardware processors configured by machine-readable, computerprogram instructions. Executing the instructions may cause the one ormore processors to predict and/or detect medical conditions (e.g., acardiac event) in real-time based on obtained biomedical signals. Insome embodiments, some or all of the features described herein may beperformed by a controller of a computing system. In some embodiments,some or all of the features described herein may be performed by one ormore other processors that are configured to execute the featuresdescribed herein by machine-readable instructions.

FIG. 7 is an example of a method 700 for predicting and/or detectingcardiac events, in accordance with various embodiments. The operationsof method 700 presented below are intended to be illustrative and, assuch, should not be viewed as limiting. In some implementations, method700 may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed. Insome implementations, two or more of the operations may occursubstantially simultaneously. The described operations may beaccomplished using some or all of the system components described indetail above.

In some embodiments, method 700 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, a central processingunit, a graphics processing unit, a controller, a microcontroller, ananalog circuit designed to process information, a state machine, and/orother mechanisms for electronically processing information). The one ormore processing devices may include one or more devices executing someor all of the operations of method 700 in response to instructionsstored electronically on one or more electronic storage mediums. The oneor more processing devices may include one or more devices configuredthrough hardware, firmware, and/or software to be specifically designedfor execution of one or more of the operations of method 700.

In an operation 702, method 700 may include obtaining training datacomprising multiple biomedical signals related to a particular medicalcondition. In various embodiments, the biomedical signals may compriseECG signals. In some embodiments, training data may be obtained from adatabase associated with a particular biomedical signal. In an exemplaryembodiment in which the biomedical signal comprises an ECG signal, thetraining data may be obtained from the Physionet/CinC 2017 database,and/or one or more other databases that store ECG signals. In someembodiments, signals obtained from individuals in real-time may bestored and later used to train a machine learning algorithm to predictand/or detect one or more medical conditions. In some implementations,operation 702 may be performed by a processor component the same as orsimilar to training component 902 (shown in FIG. 9 and describedherein).

In an operation 704, method 700 may include preprocessing the trainingsignals and signals obtained for an individual in real-time. In variousembodiments, the signals obtained may comprise ECG signals. In variousembodiments, pre-processing the training signals and the signalsobtained for an individual in real-time may comprise baseline removal,peak filtering, discretization, normalization, soft thresholding, and/orother pre-processing functions. In some embodiments, pre-processing thesignals may comprise removing baseline drift and waves of low frequencyby subtracting the moving average from each signal. In some embodiments,pre-processing signals the may comprise applying a non-linear filter toremove variations in each signal, wherein variations in the signalinclude noise. In some embodiments, pre-processing the signals maycomprise sampling each signal to a discrete signal. In some embodiments,pre-processing the signals may comprise normalizing each signal betweena first value and a second value (e.g., 0 and 1). In some embodiments,pre-processing the signals may comprise applying the soft thresholdingfunction to the signal. In some embodiments, pre-processing the signalsmay comprise renormalizing the signal according to a local relativemagnitude. In some embodiments, pre-processing the training signals maycomprise extracting from each signal a pre-event signal that spans apredetermined time interval before the cardiac event, wherein thepre-event training signals are applied to the Markov chain algorithm totrain a machine learning algorithm. In some embodiments, pre-processingthe signals may comprise identifying patterns within the signal andassigning identifiers to each pattern identified. In someimplementations, operation 704 may be performed by a processor componentthe same as or similar to pre-processing component 906 (shown in FIG. 9and described herein).

In an operation 706, method 700 may include analyzing the trainingsignals to obtain a sequence of probability vectors. For example, basedon the Markov model “M_(CE) _(i) ” for the Pre-Cardiac Event cases CE₁,. . . , CE_(r) and the Markov model “M_(n)” for the Normal case obtainedduring the training phase, conditional probabilities may be computedindicating whether a signal comprising certain patterns represents oneof the “Pre-Cardiac Event” states or a “Normal” state. In someimplementations, operation 706 may be performed by a processor componentthe same as or similar to classification component 904 or Markov chaincomponent 908 (shown in FIG. 9 and described herein).

In an operation 708, method 700 may include generating trained Markovtransition matrices based on the sequence of probability vectors andapplication of the pre-processed training signals to a Markov chainalgorithm. In various embodiments, training signals may be applied to aMarkov chain prediction algorithm to train a machine learning algorithmusing the sequence of probability vectors obtained by analyzing the ECGtraining signals, resulting in trained Markov transition matrices. Insome implementations, operation 708 may be performed by a processorcomponent the same as or similar to Markov chain component 908 (shown inFIG. 9 and described herein).

In an operation 710, method 700 may include applying the trained Markovtransition matrices to a biomedical signal of an individual to predictthe occurrence of a particular cardiac event. In various embodiments, analert may be generated and communicated when a cardiac event has beenpredicted or detected. In some implementations, operation 708 may beperformed by a processor component the same as or similar to Markovchain component 908 (shown in FIG. 9 and described herein).

As used herein, a module might be implemented utilizing any form ofhardware, software, or a combination thereof. For example, one or moreprocessors, controllers, ASICs, PLAs, PALs, CPLDs, FPGAs, logicalcomponents, software routines or other mechanisms might be implementedto make up a module. In implementation, the various modules describedherein might be implemented as discrete circuits or the functions andfeatures described can be shared in part or in total among one or morecircuits. In other words, as would be apparent to one of ordinary skillin the art after reading this description, the various features andfunctionality described herein may be implemented in any givenapplication and can be implemented in one or more separate or sharedcircuits in various combinations and permutations. Even though variousfeatures or elements of functionality may be individually described orclaimed as separate circuits, one of ordinary skill in the art willunderstand that these features and functionality can be shared among oneor more common circuits, and such description shall not require or implythat separate circuits are required to implement such features orfunctionality.

Where modules are implemented in whole or in part using software, in oneembodiment, these software elements can be implemented to operate with acomputing or processing system capable of carrying out the functionalitydescribed with respect thereto. One such example computing system isshown in FIG. 8. Various embodiments are described in terms of thisexample-computing system 800. After reading this description, it willbecome apparent to a person skilled in the relevant art how to implementthe technology using other computing systems or architectures.

Referring now to FIG. 8, computing system 800 may represent computing orprocessing capabilities within a large-scale system comprising aplurality of hardware components of various types that may communicatewithin and across partitions. Computing system 800 may also represent,for example, computing or processing capabilities found withinmainframes, supercomputers, workstations or servers; or any other typeor group of special-purpose or general-purpose computing devices as maybe desirable or appropriate for a given application or environment.Computing system 800 might also represent computing capabilitiesembedded within or otherwise available to a given device.

Computing system 800 might include, for example, one or more processors,controllers, control modules, or other processing devices, such as aprocessor 804. Processor 804 might be implemented using ageneral-purpose or special-purpose processing engine such as, forexample, a microprocessor (whether single-, dual- or multi-coreprocessor), signal processor, graphics processor (e.g., GPU) controller,or other control logic. In the illustrated example, processor 804 isconnected to a bus 802, although any communication medium can be used tofacilitate interaction with other components of computing system 800 orto communicate externally.

Computing system 800 might also include one or more memory modules,simply referred to herein as main memory 808. For example, in someembodiments random access memory (RAM) or other dynamic memory, might beused for storing information and instructions to be executed byprocessor 804. Main memory 808 might also be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 804. Computing system 800 mightlikewise include a read only memory (“ROM”) or other static storagedevice coupled to bus 802 for storing static information andinstructions for processor 804.

The computing system 800 might also include one or more various forms ofinformation storage mechanism 810, which might include, for example, amedia drive 812 and a storage unit interface 820. The media drive 812might include a drive or other mechanism to support fixed or removablestorage media 814. For example, a hard disk drive, a floppy disk drive,a magnetic tape drive, an optical disk drive, a CD or DVD drive (R orRW), a flash drive, or other removable or fixed media drive might beprovided. Accordingly, storage media 814 might include, for example, ahard disk, a floppy disk, magnetic tape, cartridge, optical disk, a CDor DVD, or other fixed or removable medium that is read by, written toor accessed by media drive 812. As these examples illustrate, thestorage media 814 can include a computer usable storage medium havingstored therein computer software or data.

In alternative embodiments, information storage mechanism 810 mightinclude other similar instrumentalities for allowing computer programsor other instructions or data to be loaded into computing system 800.Such instrumentalities might include, for example, a fixed or removablestorage unit 822 and an interface 820. Examples of such storage units822 and interfaces 820 can include a program cartridge and cartridgeinterface, a removable memory (for example, a flash memory or otherremovable memory module) and memory slot, a flash drive and associatedslot (for example, a USB drive), a PCMCIA slot and card, and other fixedor removable storage units 822 and interfaces 820 that allow softwareand data to be transferred from the storage unit 822 to computing system800.

Computing system 800 might also include a communications interface 824.Communications interface 824 might be used to allow software and data tobe transferred between computing system 800 and external devices.Examples of communications interface 824 might include a modem orsoftmodem, a network interface (such as an Ethernet, network interfacecard, WiMedia, IEEE 802.XX, Bluetooth® or other interface), acommunications port (such as for example, a USB port, IR port, RS232port, or other port), or other communications interface. Software anddata transferred via communications interface 824 might typically becarried on signals, which can be electronic, electromagnetic (whichincludes optical) or other signals capable of being exchanged by a givencommunications interface 824. These signals might be provided tocommunications interface 824 via a channel 828. This channel 828 mightcarry signals and might be implemented using a wired or wirelesscommunication medium. Some examples of a channel might include a phoneline, a cellular link, an RF link, an optical link, a network interface,a local or wide area network, and other wired or wireless communicationschannels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as, forexample, memory 808, storage unit 820, media 814, and channel 828. Theseand other various forms of computer program media or computer usablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processing device for execution. Such instructionsembodied on the medium, are generally referred to as “computer programcode” or a “computer program product” (which may be grouped in the formof computer programs or other groupings). When executed, suchinstructions might enable the computing system 800 to perform featuresor functions of the disclosed technology as discussed herein.

Referring now to FIG. 9, event prediction module 900 may be configuredto predict and/or detect medical conditions based on one or moreobtained biomedical signals, in accordance with various embodiments andfeatures described herein. The various components of event predictionmodule 900 depict various sets of functions that may be implemented bycomputer program instructions of event prediction module 900. Eventprediction module 900 may be configured to program electronic controlunit 50 and/or computer system 800 to predict and/or detect medicalconditions based on one or more obtained biomedical signals using all ora portion of the components of event prediction module 900 illustratedin FIG. 9.

Event prediction module 900 may include a training component 902, aclassification component 904, a pre-processing component 906, a Markovchain component 908, a results component 910, and/or other components.One or more of training component 902, classification component 904,pre-processing component 906, Markov chain component 908, and resultscomponent 910 may be coupled to one another or to components not shownin FIG. 9. Each of the components of event prediction module 900 maycomprise various instructions that program a computer system (e.g.,electronic control unit 50 and/or computer system 800) to performvarious operations, each of which are described in greater detailherein. As used herein, for convenience, the various instructions willbe described as performing an operation, when, in fact, the variousinstructions program the processors (and therefore computer system) toperform the operation.

While various embodiments of the disclosed technology have beendescribed above, it should be understood that they have been presentedby way of example only, and not of limitation. Likewise, the variousdiagrams may depict an example architectural or other configuration forthe disclosed technology, which is done to aid in understanding thefeatures and functionality that can be included in the disclosedtechnology. The disclosed technology is not restricted to theillustrated example architectures or configurations, but the desiredfeatures can be implemented using a variety of alternative architecturesand configurations. Indeed, it will be apparent to one of skill in theart how alternative functional, logical or physical partitioning andconfigurations can be implemented to implement the desired features ofthe technology disclosed herein. Also, a multitude of differentconstituent module names other than those depicted herein can be appliedto the various partitions. Additionally, with regard to flow diagrams,operational descriptions and method claims, the order in which the stepsare presented herein shall not mandate that various embodiments beimplemented to perform the recited functionality in the same orderunless the context dictates otherwise.

Although the disclosed technology is described above in terms of variousexemplary embodiments and implementations, it should be understood thatthe various features, aspects and functionality described in one or moreof the individual embodiments are not limited in their applicability tothe particular embodiment with which they are described, but instead canbe applied, alone or in various combinations, to one or more of theother embodiments of the disclosed technology, whether or not suchembodiments are described and whether or not such features are presentedas being a part of a described embodiment. Thus, the breadth and scopeof the technology disclosed herein should not be limited by any of theabove-described exemplary embodiments.

Terms and phrases used in this document, and variations thereof, unlessotherwise expressly stated, should be construed as open ended as opposedto limiting. As examples of the foregoing: the term “including” shouldbe read as meaning “including, without limitation” or the like; the term“example” is used to provide exemplary instances of the item indiscussion, not an exhaustive or limiting list thereof; the terms “a” or“an” should be read as meaning “at least one,” “one or more” or thelike; and adjectives such as “conventional,” “traditional,” “normal,”“standard,” “known” and terms of similar meaning should not be construedas limiting the item described to a given time period or to an itemavailable as of a given time, but instead should be read to encompassconventional, traditional, normal, or standard technologies that may beavailable or known now or at any time in the future. Likewise, wherethis document refers to technologies that would be apparent or known toone of ordinary skill in the art, such technologies encompass thoseapparent or known to the skilled artisan now or at any time in thefuture.

The presence of broadening words and phrases such as “one or more,” “atleast,” “but not limited to” or other like phrases in some instancesshall not be read to mean that the narrower case is intended or requiredin instances where such broadening phrases may be absent. The use of theterm “module” does not imply that the components or functionalitydescribed or claimed as part of the module are all configured in acommon package. Indeed, any or all of the various components of amodule, whether control logic or other components, can be combined in asingle package or separately maintained and can further be distributedin multiple groupings or packages or across multiple locations.

Additionally, the various embodiments set forth herein are described interms of exemplary block diagrams, flow charts and other illustrations.As will become apparent to one of ordinary skill in the art afterreading this document, the illustrated embodiments and their variousalternatives can be implemented without confinement to the illustratedexamples. For example, block diagrams and their accompanying descriptionshould not be construed as mandating a particular architecture orconfiguration.

What is claimed is:
 1. A system for predicting a cardiac event, thesystem comprising: one or more sensors configured to monitor abiomedical signal of an individual in real-time; one or more physicalprocessors programmed with computer program instructions that, whenexecuted by the one or more physical processors, configure the systemto: receive a trained machine learning (ML) model that relates a firstplurality of biomedical training signals to a cardiac event and a secondplurality of biomedical training signals when the cardiac event does notoccur; receive the biomedical signal of the individual, wherein thebiomedical signal is received by the one or more sensors, and whereinthe individual is a driver or a passenger of a vehicle; and determine aconditional probability of the cardiac event by applying the biomedicalsignal of the individual to the trained ML model.
 2. The system of claim1, further comprising: when the conditional probability associated withthe cardiac event is the greatest among conditional probabilities of thetrained ML model, perform an action identifying the individual willlikely experience the cardiac event.
 3. The system of claim 1, whereinthe trained machine learning (ML) model is a Markov model.
 4. The systemof claim 1, wherein the one or more physical processors arecommunicatively coupled to an electronic control unit of the vehicle. 5.The system of claim 1, wherein the sensors are affixed to the individualwhile the individual is the driver or the passenger of the vehicle. 6.The system of claim 1, wherein the ML model is trained using the firstplurality of biomedical training signals related to the cardiac event,and the second plurality of biomedical training signals when the cardiacevent does not occur.
 7. A method for predicting a cardiac event, themethod being implemented in a computer system having: one or moresensors configured to monitor a biomedical signal of an individual inreal-time; and one or more physical processors programmed with computerprogram instructions that, when executed by the one or more physicalprocessors, cause the computer system to perform the method, the methodcomprising: receiving a trained machine learning (ML) model that relatesa first plurality of biomedical training signals to a cardiac event anda second plurality of biomedical training signals when the cardiac eventdoes not occur; receiving the biomedical signal of the individual,wherein the biomedical signal is received by the one or more sensors,and wherein the individual is a driver or a passenger of a vehicle; anddetermining a conditional probability of the cardiac event by applyingthe biomedical signal of the individual to the trained ML model.
 8. Themethod of claim 7, further comprising: when the conditional probabilityassociated with the cardiac event is the greatest among conditionalprobabilities of the trained ML model, performing an action identifyingthe individual will likely experience the cardiac event.
 9. The methodof claim 7, wherein the trained machine learning (ML) model is a Markovmodel.
 10. The method of claim 7, wherein the one or more physicalprocessors are communicatively coupled to an electronic control unit ofthe vehicle.
 11. The method of claim 7, wherein the sensors are affixedto the individual while the individual is the driver or the passenger ofthe vehicle.
 12. The method of claim 7, wherein the ML model is trainedusing the first plurality of biomedical training signals related to thecardiac event, and the second plurality of biomedical training signalswhen the cardiac event does not occur.
 13. A non-transitory computerreadable media including instructions that, when executed by one or moreprocessors, cause the one or more processors to: receive a trainedmachine learning (ML) model that relates a first plurality of biomedicaltraining signals to a cardiac event and a second plurality of biomedicaltraining signals when the cardiac event does not occur; receive thebiomedical signal of the individual, wherein the biomedical signal isreceived by the one or more sensors, and wherein the individual is adriver or a passenger of a vehicle; and determine a conditionalprobability of the cardiac event by applying the biomedical signal ofthe individual to the trained ML model.
 14. The non-transitory computerreadable media of claim 13, further configured to: when the conditionalprobability associated with the cardiac event is the greatest amongconditional probabilities of the trained ML model, perform an actionidentifying the individual will likely experience the cardiac event. 15.The non-transitory computer readable media of claim 13, wherein thetrained machine learning (ML) model is a Markov model.
 16. Thenon-transitory computer readable media of claim 13, wherein the one ormore physical processors are communicatively coupled to an electroniccontrol unit of the vehicle.
 17. The non-transitory computer readablemedia of claim 13, wherein the computer readable media iscommunicatively coupled with sensors, and wherein the sensors areaffixed to the individual while the individual is the driver or thepassenger of the vehicle.
 18. The non-transitory computer readable mediaof claim 13, wherein the ML model is trained using the first pluralityof biomedical training signals related to the cardiac event, and thesecond plurality of biomedical training signals when the cardiac eventdoes not occur.